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A Class of N-Player Colonel Blotto Games with Multidimensional Private Information University of Zurich Department of Economics Working Paper Series ISSN 1664-7041 (print) ISSN 1664-705X (online) Working Paper No. 336 A Class of N-Player Colonel Blotto Games with Multidimensional Private Information Christian Ewerhart and Dan Kovenock Revised version, February 2021 A Class of N-Player Colonel Blotto Games With Multidimensional Private Information Christian Ewerhart Dan Kovenocky Department of Economics Economic Science Institute University of Zurich Chapman University Revised version: February 1, 2021 Abstract. In this paper, we study N-player Colonel Blotto games with incomplete information about battlefield valuations. Such games arise in job markets, research and development, electoral competition, security analysis, and conflict resolution. For M N + 1 battlefields, we identify a Bayes-Nash equilibrium in which the resource ≥ allocation to a given battlefield is strictly monotone in the valuation of that battlefield. We also explore extensions such as heterogeneous budgets, the case M N, full-support ≤ type distributions, and network games. Keywords. Colonel Blotto games Private information Bayes-Nash equilibrium · · · Generalized Dirichlet distributions Networks · *) Corresponding author. Postal address: Schönberggasse 1, 8001 Zurich, Switzerland. E-mail address: [email protected]. ) E-mail address: [email protected]. y 1 Introduction In a Colonel Blotto game, players simultaneously and independently allocate their en- dowments of a resource across a set of battlefields. The player that deploys the largest amount of the resource to a given battlefield scores a win and enjoys a gain in utility equivalent to her valuation of that battlefield. Thus, a player’s utility corresponds to the sum of the valuations of all battlefields won by the player. Colonel Blotto games naturally arise in a large number of applied settings, such as in job markets, research and development, electoral competition, security analysis, and conflict resolution. Colonel Blotto games also have been among the first games seriously studied in the theoretical literature [7, 8, 9]. While the case of complete information is fairly well understood [21, 20, 25, 26, 18, 29], progress has been more limited in the case of incomplete infor- mation, with very few exceptions [1, 17, 13, 3, 15]. This paper studies N-player Colonel Blotto games with M battlefields and multi- dimensional incomplete information regarding battlefield valuations. We assume that valuation vectors are private information and independently distributed across players. Only the ex-ante distribution of valuation vectors is common knowledge. Each player maximizes the expected sum of valuations of battlefields won, where resource budgets are fixed and homogeneous across players, and where unused resources do not have any positive value. In the case where the number of battlefields strictly exceeds the number of players, i.e., for M N + 1, we identify a Bayes-Nash equilibrium in which any player’s ≥ resource allocation to a battlefield is strictly monotone increasing in her valuation of that battlefield. The construction of equilibria for more than two players relies on a new dis- tributional assumption. Specifically, we exploit the particular properties of generalized Dirichlet and Liouville distributions in finite-dimensional vector spaces equipped with a 1 p-norm. We also explore several extensions. First, we touch upon the case of heterogeneous budgets. While a complete solution is beyond the scope of the present paper, we find new classes of Bayes-Nash equilibria. In one example, a player with a substantially larger budget outbids her opponent on her preferred (M 1) battlefields, while the player with the smaller budget bids only on a single preferred battlefield. Next, we seek equilibria in the case excluded by our assumptions so far, i.e., for the case M N. We find equilibria ≤ in the “crowded” case where the number of battlefields is suffi ciently small compared to the number of players. These equilibria, in which all players bid on their preferred battlefield only, are shown to exist under a fairly flexible assumption on ex-ante type distributions. Third, we study distributions with full support, which allows us to extend existing results. Fourth and finally, we discuss network games in which players may be active only in a subset of all battlefields. While the Colonel Blotto game has a certain similarity with a single-unit all-pay auc- tion [30, 12, 5, 6, 16], our analysis draws especially on three prior contributions. Kovenock and Roberson [17] presented an example with two players and three battlefields. Private valuations of battlefields are drawn independently from a uniform distribution over a two-dimensional surface in Euclidean space. Since, in that case, marginal type distribu- tions are uniform, the budget constraint may be kept by bidding the squared valuation on each battlefield. It turns out that this strategy constitutes a symmetric Bayes-Nash equilibrium. Hortala-Vallve [13] solved the case N = M = 2, where bidding exclusively on one of the highest-valuation battlefields is a weakly dominant strategy. Akyol [3] noted that rescaling a valuation vector by a positive factor does not affect a player’sbest response set. He offered an extension to any number of battlefields by assuming that individual battlefield valuations follow a generalized gamma distribution. However, he 2 still focused on the case of two players, which may be restrictive, e.g., in a job market environment. The analysis of the present paper subsumes all results obtained in prior work. Moreover, we construct equilibria with more than two players, where we use novel distributional assumptions to deal with the case M N + 1. Thus, the present pa- ≥ per goes beyond existing work by considering a wider class of examples of multi-player Colonel Blotto games with incomplete information about valuations. There are also a number of less closely related papers. In a model with N players and private information about budgets, Adamo and Matros [1] identified a symmetric monotone Bayes-Nash equilibrium. A higher budget allows a player to scale up her resource allocation, while the share of the resource allocated to individual battlefields remains constant. This leads to a tractable one-dimensional problem. Powell [23] studied a signaling game with private information about vulnerability. Next, in a model of price setting with menu costs for multiproduct firms, Alvarez and Lippi [4] made use of the marginals of a uniform distribution on a higher-dimensional Euclidean sphere that represents a vector of price changes. They, however, studied the problem of a monopolist, i.e., there is no Colonel Blotto game. Tang and Zhang [28] considered mixed extensions of normal-form games where mixed strategies correspond to points on a Euclidean sphere. Paarporn et al. [22] assumed one-sided incomplete information in a Colonel Blotto game with a finite state space. In our discussion of generalized Dirichlet and Liouville distributions, we follow Hashorva et al. [11] and Song and Gupta [27]. See also Richter [24] and Ahmadi-Javid and Moeini [2]. Gupta and Richards [10] offer an insightful historical account of Dirichlet and Liouville distributions. The rest of this paper is structured as follows. Section 2 introduces the model. Section 3 presents the main result. Extensions are discussed in Section 4. Section 5 concludes. An Appendix offers formal detail omitted from the body of the paper. 3 2 The model 2.1 Set-up and notation There are N 2 risk-neutral players, denoted by i 1,...,N , and M 2 battlefields, ≥ 2 f g ≥ denoted by j 1,...,M . Each player is endowed with an identical budget of a 2 f g perfectly divisible resource. For convenience, we normalize budgets to one. A player’s resource allocation is a vector b = (b1, . , bM ), where bj 0 denotes the amount of the resource allocated to battlefield j. We call a ≥ resource allocation b = (b1, . , bM ) feasible if M bj 1. j=1 ≤ P Denote by = M the set of feasible resource allocations over M battlefields. B B Before deciding about the resource allocation, each player privately learns her re- spective vector of battlefield valuations, v = (v1, v2, . , vM ). The vector v is commonly known to be drawn, independently across players, from a given M probability measure on (the Borel subsets of) R+ , where R+ = [0, ). Let denote 1 V the support of . Specific assumptions on and will be imposed in the statements of V the subsequent results. A strategy is a (measurable) mapping : . When adhering to strategy , type V!B v’sresource allocation is (v) = ( 1(v), . , M (v)) . 2 B Any strategy of an opponent induces a probability measure over feasible resource alloca- tions. Therefore, given strategies for the (N 1) opponents, type v’sresource allocation 4 translates into a vector of winning probabilities, and hence, into an expected payoff for type v. The N players simultaneously and independently choose feasible resource allocations. In each battlefield, the player that allocates the largest amount of the resource wins. In the case of a tie in battlefield j, each of the players that allocated the largest amount of the resource to battlefield j wins in that battlefield with equal probability. Each player’s payoff equals the sum of her valuations of the battlefields won. A strategy will be referred to as a symmetric Bayes-Nash equilibrium strategy if, for any type realization v , the resource allocation (v) maximizes the expected 2 V payoff of type v under the assumption that the other (N 1) players individually adhere to strategy . 2.2 Heuristic discussion of the player’sproblem Suppose that all opponents of Player 1 adhere to strategy = (v). Then, the marginal distribution of bids on each battlefield is identical across players i 2,...,N . We 2 f g denote the distribution function of this common probability distribution by G(bj) = Pr( j(v) bj). Provided there are no mass points in G, type v’sproblem reads ≤ max F (bj)vj, (1) (b1,...,bM ) 2B j 1,...,M 2f P g where, by independence of types across players, the cumulative distribution function of the highest bid is given as N 1 F (bj) = G(bj) .
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